This study tests issues of control in predicting the reduction of symptomatology among women at high risk for anxiety and depression. Previous research identifies women with children who are employed full time and who are housewives as high risk groups for anxious and depressive symptomatology. In contrast, women with children who work part time are identified as a low risk group. On this basis, high risk is associated with conditions of particularly high overload or of low power and access to alternatives among women, while low risk is associated with a combination of fewer demands, greater power, and greater access to alternatives. It is proposed that issues of control underlie factors of overload, power and alternative roles and account for their effect on anxious and depressive symptoms. Thus, it is hypothesized that conditions which increase control reduce symptomatology among high risk groups of women. Specifically, increased control in terms of demands or in terms of other sources such as job autonomy or personal attitudes is predicted to lower symptoms among the high risk group of women with children employed full time. Greater control in terms of resources or other bases of power, alternative roles, or personal attitudes are hypothesized to reduce symptoms among women with children who are housewives. Further, the proposed research examines the specific differences between identified low and high risk groups and differences within the low risk group in terms of demographic, employment, and familial characteristics. This analysis investigates characteristics which could alternatively account for differences in symptoms between low and high risk groups. This analysis also explores the specific conditions under which part time employment is associated with low risk of anxiety and depression for women. Two community surveys, one of a national sample, one of a regional sample, are used for this investigation. This secondary data analysis will use multivariate techniques including analysis of covariance and multiple analysis of covariance.